Explainable Software Bot Contributions: Case Study of Automated Bug Fixes

In a software project, esp. in open-source, a contribution is a valuable piece of work made to the project: writing code, reporting bugs, translating, improving documentation, creating graphics, etc. We are now at the beginning of an exciting era where software bots will make contributions that are of similar nature than those by humans. Dry contributions, with no explanation, are often ignored or rejected, because the contribution is not understandable per se, because they are not put into a larger context, because they are not grounded on idioms shared by the core community of developers. We have been operating a program repair bot called Repairnator for 2 years and noticed the problem of "dry patches": a patch that does not say which bug it fixes, or that does not explain the effects of the patch on the system. We envision program repair systems that produce an "explainable bug fix": an integrated package of at least 1) a patch, 2) its explanation in natural or controlled language, and 3) a highlight of the behavioral difference with examples. In this paper, we generalize and suggest that software bot contributions must explainable, that they must be put into the context of the global software development conversation

Analysis and Detection of Information Types of Open Source Software Issue Discussions

Most modern Issue Tracking Systems (ITSs) for open source software (OSS) projects allow users to add comments to issues. Over time, these comments accumulate into discussion threads embedded with rich information about the software project, which can potentially satisfy the diverse needs of OSS stakeholders. However, discovering and retrieving relevant information from the discussion threads is a challenging task, especially when the discussions are lengthy and the number of issues in ITSs are vast. In this paper, we address this challenge by identifying the information types presented in OSS issue discussions. Through qualitative content analysis of 15 complex issue threads across three projects hosted on GitHub, we uncovered 16 information types and created a labeled corpus containing 4656 sentences. Our investigation of supervised, automated classification techniques indicated that, when prior knowledge about the issue is available, Random Forest can effectively detect most sentence types using conversational features such as the sentence length and its position. When classifying sentences from new issues, Logistic Regression can yield satisfactory performance using textual features for certain information types, while falling short on others. Our work represents a nontrivial first step towards tools and techniques for identifying and obtaining the rich information recorded in the ITSs to support various software engineering activities and to satisfy the diverse needs of OSS stakeholders.Comment: 41st ACM/IEEE International Conference on Software Engineering (ICSE2019

International conference on software engineering and knowledge engineering: Session chair

The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing. The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome

Towards automatic context-aware summarization of code entities

Software developers are working with different methods and classes and in order to understand those that perplex them and–or that are part of their tasks, they need to tackle with a huge amount of information. Therefore, providing developers with high-quality summaries of code entities can help them during their maintenance and evolution tasks. To provide useful information about the purpose of code entities, informal documentation (Stack Overflow) has been shown to be an important source of information that can be leveraged. In this study, we investigate bug reports as a type of informal documentation and we apply machine learning to produce summaries of code entities (methods and classes) in bug reports. In the proposed approach, code entities are extracted using a technique in a form of an island parser that we implemented to identify code in bug reports. Additionally, we applied machine learning to select a set of useful sentences that will be part of the code entities’ summaries. We have used logistic regression as our machine learning technique to rank sentences based on their importance. To this aim, a corpus of sentences is built based on the occurrence of code entities in the sentences belonging to bug reports containing the code entities in question. In the last step, summaries have been evaluated using surveys to estimate the quality of produced summaries. The results show that the automatically produced summaries can reduce time and effort to understand the usage of code entities. Specifically, the majority of participants found summaries extremely helpful to decrease the understanding time (43.5%) and the effort to understand the code entities (39.1%). In the future, summaries can be produced by using other informal documentation such as mailing lists or stack overflow, etc. Additionally, the approach can be applied in practical settings. Consequently, it can be used within an IDE such as Eclipse to assist developers during their software maintenance and evolution tasks